Skip to main content
SpringerLink
Log in
Book cover

International Symposium on Neural Networks

ISNN 2013: Advances in Neural Networks – ISNN 2013 pp 395–404Cite as

Semi-Supervised Learning Using Random Walk Limiting Probabilities

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7952))

Abstract

The semi-supervised learning paradigm allows that a large amount of unlabeled data be classified using just a few labeled data. To account for the minimal a priori label knowledge, the information provided by the unlabeled data is also used in the classification process. This paper describes a semi-supervised technique that uses random walk limiting probabilities to propagate label information. Each label is propagated through a network of unlabeled instances via a biased random walk. The probability of a vertex receiving a label is expressed in terms of the limiting conditions of the walk process. Simulations show that the proposed technique is competitive with benchmarked techniques.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Chapelle, O., Schölkopf, B., Zien, A. (eds.): Semi-Supervised Learning. MIT Press, Cambridge (2006)

    Google Scholar 

  2. Wechsler, H., Kidode, M.: A random walk procedure for texture discrimination. IEEE Transactions on Pattern Analysis and Machine Intelligence 1(3), 272–280 (1979)

    Article  Google Scholar 

  3. Wechsler, H., Citron, T.: Feature extraction for texture classification. Pattern Recognition 12(5), 301–311 (1980)

    Article  Google Scholar 

  4. Grady, L.: Random walks for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(11), 1768–1783 (2006)

    Article  Google Scholar 

  5. Bul, S.R., Rabbi, M., Pelillo, M.: Content-based image retrieval with relevance feedback using random walks. Pattern Recognition 44(9), 2109–2122 (2011)

    Article  Google Scholar 

  6. Schll, J., Schll-Paschinger, E.: Classification by restricted random walks. Pattern Recognition 36(6), 1279–1290 (2003)

    Article  Google Scholar 

  7. Zhou, D., Schölkopf, B.: Learning from labeled and unlabeled data using random walks. In: Rasmussen, C.E., Bülthoff, H.H., Schölkopf, B., Giese, M.A. (eds.) DAGM 2004. LNCS, vol. 3175, pp. 237–244. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  8. Szummer, M., Jaakkola, T.: Partially labeled classification with markov random walks. In: Advances in Neural Information Processing Systems, pp. 945–952. MIT Press (2001)

    Google Scholar 

  9. Gallager, R.G.: Discrete Stochastic Processes, 1st edn. Springer (1996)

    Google Scholar 

  10. Çinlar, E.: Introduction to stochastic processes. Prentice Hall (1975)

    Google Scholar 

  11. Duin, R., Juszczak, P., Paclik, P., Pekalska, E., de Ridder, D., Tax, D., Verzakov, S.: Prtools4.1, a matlab toolbox for pattern recognition (2007)

    Google Scholar 

  12. Zhou, D., Bousquet, O., Lal, T.N., Weston, J., Schlkopf, B.: Learning with local and global consistency. In: Advances in Neural Information Processing Systems 16, vol. 16, pp. 321–328. MIT Press (2004)

    Google Scholar 

  13. Wang, F., Zhang, C.: Label propagation through linear neighborhoods. IEEE Transactions on Knowledge and Data Engineering 20(1), 55–67 (2008)

    Article  Google Scholar 

  14. Sun, J., Boyd, S., Xiao, L., Diaconis, P.: The fastest mixing markov process on a graph and a connection to a maximum variance unfolding problem. SIAM Review 48(4), 681–699 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  15. Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation 15(6), 1373–1396 (2003)

    Article  MATH  Google Scholar 

  16. Delalleau, O., Bengio, Y., Roux, N.L.: Nonparametric function induction in semi-supervised learning. In: Workshop Artif. Intell. Stat. (2005)

    Google Scholar 

  17. Zhou, D., Schölkopf, B.: Adaptive computation and machine learning. In: Discrete Regularization, pp. 237–250. MIT Press, Cambridge (2006)

    Google Scholar 

  18. Chapelle, O., Zien, A.: Semi–supervised classification by low density separation. In: Proceedings of the International Workshop on Artificial Intelligence and Statistics, pp. 57–64 (2005)

    Google Scholar 

  19. Joachims, T.: Transductive learning via spectral graph partitioning. In: Proc. Int. Conf. Mach. Learn., pp. 290–297 (2003)

    Google Scholar 

  20. Chapelle, O., Weston, J., Schölkopf, B.: Cluster Kernels for Semi-Supervised Learning. In: NIPS 2002, vol. 15, pp. 585–592. MIT Press, Cambridge (2003)

    Google Scholar 

  21. Corduneanu, A., Jaakkola, T.: Adaptive computation and machine learning. In: Data-dependent Regularization, pp. 163–190. MIT Press, Cambridge (2006)

    Google Scholar 

  22. Sindhwani, V., Niyogi, P.: Beyond the point cloud: from transductive to semi-supervised learning. In: Proc. 22nd Int. Conf. Mach. Learn., pp. 824–831 (2005)

    Google Scholar 

  23. Burges, C.J.C., Platt, J.C.: Adaptive computation and machine learning. In: Semi-supervised Learning with Conditional Harmonic Mixing, pp. 251–273. MIT Press, Cambridge (2006)

    Google Scholar 

  24. Zhu, X., Ghahramani, Z.: Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, School Comput. Sci., Carnegie Mellon Univ., Pittsburgh, PA, Tech. Rep (2002)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Mathematics and Computer Science, University of São Paulo, São Carlos, Brazil

    Thiago Henrique Cupertino & Liang Zhao

Authors
  1. Thiago Henrique Cupertino
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Liang Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. School of Information and Communication Engineering, Dalian University of Technology, A 530, Chuangxinyuan Building, 116023, Dalian, China

    Chengan Guo

  2. Institute of Automation, Chinese Academy of Sciences, 100864, Beijing, China

    Zeng-Guang Hou

  3. School of Automation, Huazhong University of Science and Technology, 430074, Wuhan, China

    Zhigang Zeng

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Cupertino, T.H., Zhao, L. (2013). Semi-Supervised Learning Using Random Walk Limiting Probabilities. In: Guo, C., Hou, ZG., Zeng, Z. (eds) Advances in Neural Networks – ISNN 2013. ISNN 2013. Lecture Notes in Computer Science, vol 7952. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39068-5_48

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-39068-5_48

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39067-8

  • Online ISBN: 978-3-642-39068-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation